Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [14]:
%%javascript
IPython.OutputArea.auto_scroll_threshold = 9999;
In [15]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [16]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[16]:
<matplotlib.image.AxesImage at 0x7f471288fc88>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [17]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[17]:
<matplotlib.image.AxesImage at 0x7f4720df5438>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [18]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [19]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='real_input')
    z    = tf.placeholder(tf.float32, (None, z_dim), name='z_data')
    lr   = tf.placeholder(tf.float32, None, name='learn_rate')

    return (real, z, lr)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [20]:
def discriminator(images, reuse=False, alpha=0.2):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse=reuse):
        layer_1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu_1  = tf.maximum(alpha * layer_1, layer_1)
        
        layer_2 = tf.layers.conv2d(relu_1, 128, 5, strides=2, padding='same')
        batch_2 = tf.layers.batch_normalization(layer_2, training=True)
        relu_2  = tf.maximum(alpha * batch_2, batch_2)

        layer_3 = tf.layers.conv2d(relu_2, 256, 5, strides=2, padding='same')
        batch_3 = tf.layers.batch_normalization(layer_3, training=True)
        relu_3  = tf.maximum(alpha * batch_3, batch_3)
        
        compact = tf.reshape(relu_3, (-1, 4 * 4 * 256))
        logits  = tf.layers.dense(compact, 1)
        output  = tf.sigmoid(logits)
    return output, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [21]:
def generator(z, out_channel_dim, is_train=True, alpha=0.2):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
   
    with tf.variable_scope('generator', reuse=not is_train):
        layer_1 = tf.layers.dense(z, 7 * 7 * 512)
        layer_1 = tf.reshape(layer_1, (-1, 7, 7, 512))
        layer_1 = tf.layers.batch_normalization(layer_1, training=is_train)
        layer_1 = tf.maximum(alpha * layer_1, layer_1)
        
        layer_2 = tf.layers.conv2d_transpose(layer_1, 256, 5, strides=2, padding='same')
        layer_2 = tf.layers.batch_normalization(layer_2, training=is_train)
        layer_2 = tf.maximum(alpha * layer_2, layer_2)
    
        layer_3 = tf.layers.conv2d_transpose(layer_2, 128, 5, strides=1, padding='same')
        layer_3 = tf.layers.batch_normalization(layer_3, training=is_train)
        layer_3 = tf.maximum(alpha * layer_3, layer_3)
        
        logits  = tf.layers.conv2d_transpose(layer_3, out_channel_dim, 5, strides=2, padding='same') 
        
        output  = tf.tanh(logits)
    return output


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [22]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    generator_model = generator(z=input_z, out_channel_dim=out_channel_dim)
    
    real_discriminator, real_logits = discriminator(images=input_real, reuse=False)
    fake_discriminator, fake_logits = discriminator(images=generator_model, reuse=True)
    
    real_discriminator_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=real_logits, 
                                                labels=tf.ones_like(real_discriminator)))
    
    fake_discriminator_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_logits, 
                                                labels=tf.zeros_like(fake_discriminator)))
    
    generator_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_logits, 
                                                labels=tf.ones_like(fake_discriminator)))
    
    discriminator_loss = real_discriminator_loss + fake_discriminator_loss
    
    return discriminator_loss, generator_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [23]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    trainable_vars     = tf.trainable_variables()
    discriminator_vars = [var for var in trainable_vars if var.name.startswith('discriminator')]
    generator_vars     = [var for var in trainable_vars if var.name.startswith('generator')]
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        discriminator_training_optimizer = tf.train.AdamOptimizer(
            learning_rate, beta1=beta1).minimize(d_loss, var_list=discriminator_vars)
        
        generator_training_optimizer = tf.train.AdamOptimizer(
            learning_rate, beta1=beta1).minimize(g_loss, var_list=generator_vars)
        
    return discriminator_training_optimizer, generator_training_optimizer


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [24]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [25]:
class GAN(object):
    """
    Abstraction around:
    model_inputs
    model_loss
    model_opt
    """
    def __init__(self, data_shape, z_dim, learning_rate, beta1=0.5):
        height = data_shape[1]
        width = data_shape[2]
        channels = data_shape[3]
        print('height: {}, width: {}, channels: {}, z_dim: {}'.format(
                height, width, channels, z_dim))
        
        self.input_real, self.input_z, self.learning_rate = model_inputs(
            width, height, channels, z_dim)
        self.discriminator_loss, self.generator_loss = model_loss(
            self.input_real, self.input_z, channels)
        self.discriminator_opt, self.generator_opt = model_opt(
            self.discriminator_loss, self.generator_loss, self.learning_rate, beta1)
        
def build_feed_dict(network, batch_images, rand_z, learning_rate):
    feed_dict = {
        network.input_real: batch_images, 
        network.input_z: rand_z, 
        network.learning_rate: learning_rate
    }
    return feed_dict

def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    network = GAN(data_shape, z_dim, learning_rate, beta1)
    
    saver = tf.train.Saver()
    
    samples, losses = [], []
    steps = 0
    
    print_progress = 25
    print_image    = 100
    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch in range(epoch_count):
            for batch_images in get_batches(batch_size):
                batch_images *= 2
                steps += 1
            
                rand_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run optimizers
                sess.run(network.discriminator_opt, 
                     feed_dict=build_feed_dict(network, batch_images, rand_z, learning_rate))
                
                sess.run(network.generator_opt,
                     feed_dict=build_feed_dict(network, batch_images, rand_z, learning_rate))
                
                sess.run(network.generator_opt,
                     feed_dict=build_feed_dict(network, batch_images, rand_z, learning_rate))
                
                if steps % print_progress == 0:
                    discriminator_train_loss = network.discriminator_loss.eval(
                        build_feed_dict(network, batch_images, rand_z, learning_rate))
    
                    generator_train_loss = network.generator_loss.eval(
                        build_feed_dict(network, batch_images, rand_z, learning_rate))
        
                    print("Epoch {}/{}...".format(epoch+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(discriminator_train_loss),
                          "Generator Loss: {:.4f}".format(generator_train_loss))
            
                    losses.append((discriminator_train_loss, generator_train_loss))

                if steps % print_image == 0:
                    show_generator_output(sess, 10, network.input_z, data_shape[3], data_image_mode)

        saver.save(sess, './checkpoints/generator.ckpt')
        
    return losses, samples

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [26]:
batch_size    = 128
z_dim         = 100
learning_rate = 0.001
beta1         = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
height: 28, width: 28, channels: 1, z_dim: 100
Epoch 1/2... Discriminator Loss: 2.5054... Generator Loss: 0.3928
Epoch 1/2... Discriminator Loss: 1.2978... Generator Loss: 0.5942
Epoch 1/2... Discriminator Loss: 1.6886... Generator Loss: 0.3602
Epoch 1/2... Discriminator Loss: 1.5898... Generator Loss: 0.7284
Epoch 1/2... Discriminator Loss: 1.6342... Generator Loss: 1.0899
Epoch 1/2... Discriminator Loss: 1.6576... Generator Loss: 0.3960
Epoch 1/2... Discriminator Loss: 1.6408... Generator Loss: 0.8028
Epoch 1/2... Discriminator Loss: 1.4369... Generator Loss: 0.5731
Epoch 1/2... Discriminator Loss: 1.5711... Generator Loss: 0.9489
Epoch 1/2... Discriminator Loss: 1.4811... Generator Loss: 0.9527
Epoch 1/2... Discriminator Loss: 1.4738... Generator Loss: 0.7194
Epoch 1/2... Discriminator Loss: 1.5438... Generator Loss: 1.0961
Epoch 1/2... Discriminator Loss: 1.4553... Generator Loss: 0.6039
Epoch 1/2... Discriminator Loss: 1.3909... Generator Loss: 0.5243
Epoch 1/2... Discriminator Loss: 1.4118... Generator Loss: 0.6049
Epoch 1/2... Discriminator Loss: 1.4786... Generator Loss: 0.9276
Epoch 1/2... Discriminator Loss: 1.5165... Generator Loss: 0.9801
Epoch 1/2... Discriminator Loss: 1.5482... Generator Loss: 0.9799
Epoch 2/2... Discriminator Loss: 1.4891... Generator Loss: 0.7587
Epoch 2/2... Discriminator Loss: 1.5181... Generator Loss: 0.4250
Epoch 2/2... Discriminator Loss: 1.3406... Generator Loss: 0.6638
Epoch 2/2... Discriminator Loss: 1.4263... Generator Loss: 0.5554
Epoch 2/2... Discriminator Loss: 1.5353... Generator Loss: 1.1102
Epoch 2/2... Discriminator Loss: 1.4103... Generator Loss: 0.8111
Epoch 2/2... Discriminator Loss: 1.3690... Generator Loss: 0.6052
Epoch 2/2... Discriminator Loss: 1.5201... Generator Loss: 0.3762
Epoch 2/2... Discriminator Loss: 1.4658... Generator Loss: 0.5023
Epoch 2/2... Discriminator Loss: 1.3823... Generator Loss: 0.6535
Epoch 2/2... Discriminator Loss: 1.4728... Generator Loss: 0.4560
Epoch 2/2... Discriminator Loss: 1.5132... Generator Loss: 0.4833
Epoch 2/2... Discriminator Loss: 1.4932... Generator Loss: 0.5374
Epoch 2/2... Discriminator Loss: 1.3979... Generator Loss: 0.5583
Epoch 2/2... Discriminator Loss: 1.4329... Generator Loss: 0.8007
Epoch 2/2... Discriminator Loss: 1.5083... Generator Loss: 0.3827
Epoch 2/2... Discriminator Loss: 1.4738... Generator Loss: 0.5300
Epoch 2/2... Discriminator Loss: 1.4425... Generator Loss: 0.5221
Epoch 2/2... Discriminator Loss: 1.4090... Generator Loss: 0.6000

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [28]:
batch_size    = 64
z_dim         = 200
learning_rate = 0.0001
beta1         = 0.1


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 5

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
height: 28, width: 28, channels: 3, z_dim: 200
Epoch 1/5... Discriminator Loss: 1.7543... Generator Loss: 0.6136
Epoch 1/5... Discriminator Loss: 2.8131... Generator Loss: 0.1051
Epoch 1/5... Discriminator Loss: 2.3105... Generator Loss: 0.1984
Epoch 1/5... Discriminator Loss: 2.3572... Generator Loss: 0.3418
Epoch 1/5... Discriminator Loss: 2.0111... Generator Loss: 0.2531
Epoch 1/5... Discriminator Loss: 1.8003... Generator Loss: 0.3359
Epoch 1/5... Discriminator Loss: 1.6848... Generator Loss: 0.5706
Epoch 1/5... Discriminator Loss: 1.9162... Generator Loss: 0.3827
Epoch 1/5... Discriminator Loss: 1.9321... Generator Loss: 0.6711
Epoch 1/5... Discriminator Loss: 1.7608... Generator Loss: 0.3639
Epoch 1/5... Discriminator Loss: 1.7607... Generator Loss: 0.3596
Epoch 1/5... Discriminator Loss: 1.7353... Generator Loss: 0.6082
Epoch 1/5... Discriminator Loss: 1.7724... Generator Loss: 0.3068
Epoch 1/5... Discriminator Loss: 1.7204... Generator Loss: 0.4426
Epoch 1/5... Discriminator Loss: 1.6982... Generator Loss: 0.5023
Epoch 1/5... Discriminator Loss: 1.6599... Generator Loss: 0.4795
Epoch 1/5... Discriminator Loss: 1.6160... Generator Loss: 0.5305
Epoch 1/5... Discriminator Loss: 1.6041... Generator Loss: 0.4620
Epoch 1/5... Discriminator Loss: 1.5506... Generator Loss: 0.5560
Epoch 1/5... Discriminator Loss: 1.5971... Generator Loss: 0.4765
Epoch 1/5... Discriminator Loss: 1.6826... Generator Loss: 0.4125
Epoch 1/5... Discriminator Loss: 1.5141... Generator Loss: 0.5730
Epoch 1/5... Discriminator Loss: 1.5932... Generator Loss: 0.6530
Epoch 1/5... Discriminator Loss: 1.5838... Generator Loss: 0.5522
Epoch 1/5... Discriminator Loss: 1.5764... Generator Loss: 0.5985
Epoch 1/5... Discriminator Loss: 1.5314... Generator Loss: 0.5827
Epoch 1/5... Discriminator Loss: 1.5068... Generator Loss: 0.5602
Epoch 1/5... Discriminator Loss: 1.5875... Generator Loss: 0.6741
Epoch 1/5... Discriminator Loss: 1.4972... Generator Loss: 0.7232
Epoch 1/5... Discriminator Loss: 1.5025... Generator Loss: 0.6237
Epoch 1/5... Discriminator Loss: 1.5147... Generator Loss: 0.5513
Epoch 1/5... Discriminator Loss: 1.5494... Generator Loss: 0.5431
Epoch 1/5... Discriminator Loss: 1.5091... Generator Loss: 0.5589
Epoch 1/5... Discriminator Loss: 1.5491... Generator Loss: 0.5619
Epoch 1/5... Discriminator Loss: 1.5334... Generator Loss: 0.5335
Epoch 1/5... Discriminator Loss: 1.5516... Generator Loss: 0.6541
Epoch 1/5... Discriminator Loss: 1.5246... Generator Loss: 0.5492
Epoch 1/5... Discriminator Loss: 1.5131... Generator Loss: 0.6412
Epoch 1/5... Discriminator Loss: 1.5410... Generator Loss: 0.5445
Epoch 1/5... Discriminator Loss: 1.5155... Generator Loss: 0.5068
Epoch 1/5... Discriminator Loss: 1.5014... Generator Loss: 0.6026
Epoch 1/5... Discriminator Loss: 1.4964... Generator Loss: 0.5372
Epoch 1/5... Discriminator Loss: 1.4779... Generator Loss: 0.6388
Epoch 1/5... Discriminator Loss: 1.4674... Generator Loss: 0.6454
Epoch 1/5... Discriminator Loss: 1.5715... Generator Loss: 0.5680
Epoch 1/5... Discriminator Loss: 1.4968... Generator Loss: 0.6237
Epoch 1/5... Discriminator Loss: 1.4991... Generator Loss: 0.5973
Epoch 1/5... Discriminator Loss: 1.4752... Generator Loss: 0.6156
Epoch 1/5... Discriminator Loss: 1.4757... Generator Loss: 0.5897
Epoch 1/5... Discriminator Loss: 1.4917... Generator Loss: 0.5209
Epoch 1/5... Discriminator Loss: 1.5073... Generator Loss: 0.5535
Epoch 1/5... Discriminator Loss: 1.4911... Generator Loss: 0.6262
Epoch 1/5... Discriminator Loss: 1.4936... Generator Loss: 0.6149
Epoch 1/5... Discriminator Loss: 1.4762... Generator Loss: 0.5513
Epoch 1/5... Discriminator Loss: 1.4948... Generator Loss: 0.6499
Epoch 1/5... Discriminator Loss: 1.4726... Generator Loss: 0.6372
Epoch 1/5... Discriminator Loss: 1.5084... Generator Loss: 0.5327
Epoch 1/5... Discriminator Loss: 1.4822... Generator Loss: 0.5548
Epoch 1/5... Discriminator Loss: 1.4676... Generator Loss: 0.6536
Epoch 1/5... Discriminator Loss: 1.4720... Generator Loss: 0.5412
Epoch 1/5... Discriminator Loss: 1.4753... Generator Loss: 0.5896
Epoch 1/5... Discriminator Loss: 1.4980... Generator Loss: 0.5987
Epoch 1/5... Discriminator Loss: 1.4766... Generator Loss: 0.5936
Epoch 1/5... Discriminator Loss: 1.4624... Generator Loss: 0.6784
Epoch 1/5... Discriminator Loss: 1.4944... Generator Loss: 0.5531
Epoch 1/5... Discriminator Loss: 1.4908... Generator Loss: 0.6324
Epoch 1/5... Discriminator Loss: 1.4588... Generator Loss: 0.6628
Epoch 1/5... Discriminator Loss: 1.4746... Generator Loss: 0.5650
Epoch 1/5... Discriminator Loss: 1.4380... Generator Loss: 0.6519
Epoch 1/5... Discriminator Loss: 1.4724... Generator Loss: 0.6024
Epoch 1/5... Discriminator Loss: 1.4774... Generator Loss: 0.6005
Epoch 1/5... Discriminator Loss: 1.5091... Generator Loss: 0.5499
Epoch 1/5... Discriminator Loss: 1.4582... Generator Loss: 0.5861
Epoch 1/5... Discriminator Loss: 1.4756... Generator Loss: 0.6295
Epoch 1/5... Discriminator Loss: 1.4791... Generator Loss: 0.6043
Epoch 1/5... Discriminator Loss: 1.4755... Generator Loss: 0.6279
Epoch 1/5... Discriminator Loss: 1.4610... Generator Loss: 0.7133
Epoch 1/5... Discriminator Loss: 1.4797... Generator Loss: 0.6325
Epoch 1/5... Discriminator Loss: 1.4350... Generator Loss: 0.6310
Epoch 1/5... Discriminator Loss: 1.4478... Generator Loss: 0.6266
Epoch 1/5... Discriminator Loss: 1.4970... Generator Loss: 0.7136
Epoch 1/5... Discriminator Loss: 1.4627... Generator Loss: 0.7015
Epoch 1/5... Discriminator Loss: 1.4208... Generator Loss: 0.6295
Epoch 1/5... Discriminator Loss: 1.4548... Generator Loss: 0.6315
Epoch 1/5... Discriminator Loss: 1.4658... Generator Loss: 0.5993
Epoch 1/5... Discriminator Loss: 1.4565... Generator Loss: 0.6824
Epoch 1/5... Discriminator Loss: 1.4574... Generator Loss: 0.6559
Epoch 1/5... Discriminator Loss: 1.4967... Generator Loss: 0.6744
Epoch 1/5... Discriminator Loss: 1.4662... Generator Loss: 0.6676
Epoch 1/5... Discriminator Loss: 1.4698... Generator Loss: 0.6044
Epoch 1/5... Discriminator Loss: 1.4534... Generator Loss: 0.6768
Epoch 1/5... Discriminator Loss: 1.4341... Generator Loss: 0.7052
Epoch 1/5... Discriminator Loss: 1.4512... Generator Loss: 0.5953
Epoch 1/5... Discriminator Loss: 1.4537... Generator Loss: 0.6241
Epoch 1/5... Discriminator Loss: 1.4673... Generator Loss: 0.6033
Epoch 1/5... Discriminator Loss: 1.4567... Generator Loss: 0.6226
Epoch 1/5... Discriminator Loss: 1.4556... Generator Loss: 0.5910
Epoch 1/5... Discriminator Loss: 1.4753... Generator Loss: 0.5690
Epoch 1/5... Discriminator Loss: 1.4623... Generator Loss: 0.5938
Epoch 1/5... Discriminator Loss: 1.4459... Generator Loss: 0.6832
Epoch 1/5... Discriminator Loss: 1.4615... Generator Loss: 0.6609
Epoch 1/5... Discriminator Loss: 1.4536... Generator Loss: 0.6714
Epoch 1/5... Discriminator Loss: 1.4893... Generator Loss: 0.6427
Epoch 1/5... Discriminator Loss: 1.4314... Generator Loss: 0.6481
Epoch 1/5... Discriminator Loss: 1.4430... Generator Loss: 0.6046
Epoch 1/5... Discriminator Loss: 1.4608... Generator Loss: 0.5651
Epoch 1/5... Discriminator Loss: 1.4193... Generator Loss: 0.6385
Epoch 1/5... Discriminator Loss: 1.4371... Generator Loss: 0.6208
Epoch 1/5... Discriminator Loss: 1.4771... Generator Loss: 0.6462
Epoch 1/5... Discriminator Loss: 1.4711... Generator Loss: 0.6647
Epoch 1/5... Discriminator Loss: 1.4466... Generator Loss: 0.6468
Epoch 1/5... Discriminator Loss: 1.4435... Generator Loss: 0.5728
Epoch 1/5... Discriminator Loss: 1.4686... Generator Loss: 0.5987
Epoch 1/5... Discriminator Loss: 1.4625... Generator Loss: 0.6115
Epoch 1/5... Discriminator Loss: 1.4370... Generator Loss: 0.6307
Epoch 1/5... Discriminator Loss: 1.4634... Generator Loss: 0.6801
Epoch 1/5... Discriminator Loss: 1.4449... Generator Loss: 0.5878
Epoch 1/5... Discriminator Loss: 1.4516... Generator Loss: 0.6848
Epoch 1/5... Discriminator Loss: 1.4600... Generator Loss: 0.6300
Epoch 1/5... Discriminator Loss: 1.4412... Generator Loss: 0.6279
Epoch 1/5... Discriminator Loss: 1.4878... Generator Loss: 0.5207
Epoch 1/5... Discriminator Loss: 1.4543... Generator Loss: 0.6987
Epoch 1/5... Discriminator Loss: 1.4755... Generator Loss: 0.5965
Epoch 1/5... Discriminator Loss: 1.4364... Generator Loss: 0.6363
Epoch 1/5... Discriminator Loss: 1.4279... Generator Loss: 0.6214
Epoch 1/5... Discriminator Loss: 1.4685... Generator Loss: 0.5818
Epoch 2/5... Discriminator Loss: 1.4529... Generator Loss: 0.5905
Epoch 2/5... Discriminator Loss: 1.4446... Generator Loss: 0.6086
Epoch 2/5... Discriminator Loss: 1.4897... Generator Loss: 0.5314
Epoch 2/5... Discriminator Loss: 1.4740... Generator Loss: 0.5607
Epoch 2/5... Discriminator Loss: 1.4419... Generator Loss: 0.6748
Epoch 2/5... Discriminator Loss: 1.4473... Generator Loss: 0.6817
Epoch 2/5... Discriminator Loss: 1.4621... Generator Loss: 0.6147
Epoch 2/5... Discriminator Loss: 1.4393... Generator Loss: 0.6827
Epoch 2/5... Discriminator Loss: 1.4663... Generator Loss: 0.6996
Epoch 2/5... Discriminator Loss: 1.4468... Generator Loss: 0.5707
Epoch 2/5... Discriminator Loss: 1.4514... Generator Loss: 0.6235
Epoch 2/5... Discriminator Loss: 1.4560... Generator Loss: 0.6323
Epoch 2/5... Discriminator Loss: 1.4438... Generator Loss: 0.7442
Epoch 2/5... Discriminator Loss: 1.4391... Generator Loss: 0.5717
Epoch 2/5... Discriminator Loss: 1.4638... Generator Loss: 0.6370
Epoch 2/5... Discriminator Loss: 1.4440... Generator Loss: 0.5748
Epoch 2/5... Discriminator Loss: 1.4464... Generator Loss: 0.6548
Epoch 2/5... Discriminator Loss: 1.4419... Generator Loss: 0.6738
Epoch 2/5... Discriminator Loss: 1.4512... Generator Loss: 0.7445
Epoch 2/5... Discriminator Loss: 1.4450... Generator Loss: 0.6966
Epoch 2/5... Discriminator Loss: 1.4341... Generator Loss: 0.6559
Epoch 2/5... Discriminator Loss: 1.4215... Generator Loss: 0.6893
Epoch 2/5... Discriminator Loss: 1.4527... Generator Loss: 0.6255
Epoch 2/5... Discriminator Loss: 1.4573... Generator Loss: 0.5608
Epoch 2/5... Discriminator Loss: 1.4704... Generator Loss: 0.6312
Epoch 2/5... Discriminator Loss: 1.4632... Generator Loss: 0.6416
Epoch 2/5... Discriminator Loss: 1.4690... Generator Loss: 0.6036
Epoch 2/5... Discriminator Loss: 1.4356... Generator Loss: 0.6360
Epoch 2/5... Discriminator Loss: 1.4257... Generator Loss: 0.6202
Epoch 2/5... Discriminator Loss: 1.4337... Generator Loss: 0.7369
Epoch 2/5... Discriminator Loss: 1.4322... Generator Loss: 0.6611
Epoch 2/5... Discriminator Loss: 1.4456... Generator Loss: 0.6146
Epoch 2/5... Discriminator Loss: 1.4417... Generator Loss: 0.5707
Epoch 2/5... Discriminator Loss: 1.4577... Generator Loss: 0.6076
Epoch 2/5... Discriminator Loss: 1.4394... Generator Loss: 0.6213
Epoch 2/5... Discriminator Loss: 1.4298... Generator Loss: 0.6404
Epoch 2/5... Discriminator Loss: 1.4291... Generator Loss: 0.7051
Epoch 2/5... Discriminator Loss: 1.4417... Generator Loss: 0.5780
Epoch 2/5... Discriminator Loss: 1.4595... Generator Loss: 0.7570
Epoch 2/5... Discriminator Loss: 1.4195... Generator Loss: 0.6963
Epoch 2/5... Discriminator Loss: 1.4309... Generator Loss: 0.6524
Epoch 2/5... Discriminator Loss: 1.4605... Generator Loss: 0.6137
Epoch 2/5... Discriminator Loss: 1.4288... Generator Loss: 0.6591
Epoch 2/5... Discriminator Loss: 1.4305... Generator Loss: 0.6402
Epoch 2/5... Discriminator Loss: 1.4369... Generator Loss: 0.5908
Epoch 2/5... Discriminator Loss: 1.4457... Generator Loss: 0.5807
Epoch 2/5... Discriminator Loss: 1.4123... Generator Loss: 0.6904
Epoch 2/5... Discriminator Loss: 1.4407... Generator Loss: 0.6035
Epoch 2/5... Discriminator Loss: 1.4417... Generator Loss: 0.5656
Epoch 2/5... Discriminator Loss: 1.4166... Generator Loss: 0.6262
Epoch 2/5... Discriminator Loss: 1.4296... Generator Loss: 0.7026
Epoch 2/5... Discriminator Loss: 1.4210... Generator Loss: 0.6103
Epoch 2/5... Discriminator Loss: 1.4308... Generator Loss: 0.6850
Epoch 2/5... Discriminator Loss: 1.4460... Generator Loss: 0.6266
Epoch 2/5... Discriminator Loss: 1.4500... Generator Loss: 0.6194
Epoch 2/5... Discriminator Loss: 1.4318... Generator Loss: 0.6379
Epoch 2/5... Discriminator Loss: 1.4358... Generator Loss: 0.6533
Epoch 2/5... Discriminator Loss: 1.4659... Generator Loss: 0.5573
Epoch 2/5... Discriminator Loss: 1.4063... Generator Loss: 0.6931
Epoch 2/5... Discriminator Loss: 1.4751... Generator Loss: 0.7100
Epoch 2/5... Discriminator Loss: 1.4337... Generator Loss: 0.6825
Epoch 2/5... Discriminator Loss: 1.4426... Generator Loss: 0.6456
Epoch 2/5... Discriminator Loss: 1.4270... Generator Loss: 0.6572
Epoch 2/5... Discriminator Loss: 1.4585... Generator Loss: 0.7305
Epoch 2/5... Discriminator Loss: 1.4129... Generator Loss: 0.6994
Epoch 2/5... Discriminator Loss: 1.4187... Generator Loss: 0.6558
Epoch 2/5... Discriminator Loss: 1.4192... Generator Loss: 0.6441
Epoch 2/5... Discriminator Loss: 1.4202... Generator Loss: 0.6494
Epoch 2/5... Discriminator Loss: 1.4400... Generator Loss: 0.6425
Epoch 2/5... Discriminator Loss: 1.4196... Generator Loss: 0.7206
Epoch 2/5... Discriminator Loss: 1.4198... Generator Loss: 0.6697
Epoch 2/5... Discriminator Loss: 1.4411... Generator Loss: 0.6259
Epoch 2/5... Discriminator Loss: 1.4466... Generator Loss: 0.6460
Epoch 2/5... Discriminator Loss: 1.4340... Generator Loss: 0.6293
Epoch 2/5... Discriminator Loss: 1.4193... Generator Loss: 0.6186
Epoch 2/5... Discriminator Loss: 1.4282... Generator Loss: 0.6196
Epoch 2/5... Discriminator Loss: 1.4243... Generator Loss: 0.6836
Epoch 2/5... Discriminator Loss: 1.4060... Generator Loss: 0.6657
Epoch 2/5... Discriminator Loss: 1.4076... Generator Loss: 0.6450
Epoch 2/5... Discriminator Loss: 1.4188... Generator Loss: 0.6881
Epoch 2/5... Discriminator Loss: 1.4430... Generator Loss: 0.5692
Epoch 2/5... Discriminator Loss: 1.4298... Generator Loss: 0.6345
Epoch 2/5... Discriminator Loss: 1.4466... Generator Loss: 0.7067
Epoch 2/5... Discriminator Loss: 1.4369... Generator Loss: 0.6821
Epoch 2/5... Discriminator Loss: 1.4222... Generator Loss: 0.7268
Epoch 2/5... Discriminator Loss: 1.4453... Generator Loss: 0.6317
Epoch 2/5... Discriminator Loss: 1.4212... Generator Loss: 0.6364
Epoch 2/5... Discriminator Loss: 1.4584... Generator Loss: 0.6779
Epoch 2/5... Discriminator Loss: 1.4547... Generator Loss: 0.5951
Epoch 2/5... Discriminator Loss: 1.4205... Generator Loss: 0.5961
Epoch 2/5... Discriminator Loss: 1.4532... Generator Loss: 0.6194
Epoch 2/5... Discriminator Loss: 1.4268... Generator Loss: 0.6687
Epoch 2/5... Discriminator Loss: 1.4327... Generator Loss: 0.5981
Epoch 2/5... Discriminator Loss: 1.4207... Generator Loss: 0.6496
Epoch 2/5... Discriminator Loss: 1.4308... Generator Loss: 0.6266
Epoch 2/5... Discriminator Loss: 1.4604... Generator Loss: 0.5400
Epoch 2/5... Discriminator Loss: 1.4371... Generator Loss: 0.5625
Epoch 2/5... Discriminator Loss: 1.4105... Generator Loss: 0.6035
Epoch 2/5... Discriminator Loss: 1.4190... Generator Loss: 0.6160
Epoch 2/5... Discriminator Loss: 1.4209... Generator Loss: 0.6530
Epoch 2/5... Discriminator Loss: 1.4268... Generator Loss: 0.7138
Epoch 2/5... Discriminator Loss: 1.4209... Generator Loss: 0.6508
Epoch 2/5... Discriminator Loss: 1.4335... Generator Loss: 0.6230
Epoch 2/5... Discriminator Loss: 1.4420... Generator Loss: 0.7115
Epoch 2/5... Discriminator Loss: 1.4280... Generator Loss: 0.6501
Epoch 2/5... Discriminator Loss: 1.4388... Generator Loss: 0.6822
Epoch 2/5... Discriminator Loss: 1.4212... Generator Loss: 0.7037
Epoch 2/5... Discriminator Loss: 1.4152... Generator Loss: 0.6686
Epoch 2/5... Discriminator Loss: 1.4143... Generator Loss: 0.6757
Epoch 2/5... Discriminator Loss: 1.4159... Generator Loss: 0.6428
Epoch 2/5... Discriminator Loss: 1.4339... Generator Loss: 0.6220
Epoch 2/5... Discriminator Loss: 1.4225... Generator Loss: 0.6750
Epoch 2/5... Discriminator Loss: 1.4220... Generator Loss: 0.6131
Epoch 2/5... Discriminator Loss: 1.4100... Generator Loss: 0.6409
Epoch 2/5... Discriminator Loss: 1.4111... Generator Loss: 0.6801
Epoch 2/5... Discriminator Loss: 1.4356... Generator Loss: 0.5685
Epoch 2/5... Discriminator Loss: 1.4261... Generator Loss: 0.6366
Epoch 2/5... Discriminator Loss: 1.4460... Generator Loss: 0.6160
Epoch 2/5... Discriminator Loss: 1.4231... Generator Loss: 0.6838
Epoch 2/5... Discriminator Loss: 1.4418... Generator Loss: 0.6237
Epoch 2/5... Discriminator Loss: 1.4136... Generator Loss: 0.5995
Epoch 2/5... Discriminator Loss: 1.4110... Generator Loss: 0.7639
Epoch 2/5... Discriminator Loss: 1.4340... Generator Loss: 0.6636
Epoch 2/5... Discriminator Loss: 1.4134... Generator Loss: 0.6388
Epoch 2/5... Discriminator Loss: 1.4393... Generator Loss: 0.6216
Epoch 2/5... Discriminator Loss: 1.4124... Generator Loss: 0.6582
Epoch 2/5... Discriminator Loss: 1.4149... Generator Loss: 0.6303
Epoch 3/5... Discriminator Loss: 1.4273... Generator Loss: 0.6285
Epoch 3/5... Discriminator Loss: 1.4128... Generator Loss: 0.6651
Epoch 3/5... Discriminator Loss: 1.4076... Generator Loss: 0.6656
Epoch 3/5... Discriminator Loss: 1.4301... Generator Loss: 0.6178
Epoch 3/5... Discriminator Loss: 1.4344... Generator Loss: 0.6316
Epoch 3/5... Discriminator Loss: 1.4192... Generator Loss: 0.5821
Epoch 3/5... Discriminator Loss: 1.4226... Generator Loss: 0.6147
Epoch 3/5... Discriminator Loss: 1.4173... Generator Loss: 0.6834
Epoch 3/5... Discriminator Loss: 1.4196... Generator Loss: 0.6612
Epoch 3/5... Discriminator Loss: 1.4237... Generator Loss: 0.6487
Epoch 3/5... Discriminator Loss: 1.4320... Generator Loss: 0.6399
Epoch 3/5... Discriminator Loss: 1.4357... Generator Loss: 0.5969
Epoch 3/5... Discriminator Loss: 1.4335... Generator Loss: 0.6741
Epoch 3/5... Discriminator Loss: 1.4103... Generator Loss: 0.6960
Epoch 3/5... Discriminator Loss: 1.4263... Generator Loss: 0.6410
Epoch 3/5... Discriminator Loss: 1.4273... Generator Loss: 0.7182
Epoch 3/5... Discriminator Loss: 1.4141... Generator Loss: 0.6191
Epoch 3/5... Discriminator Loss: 1.4251... Generator Loss: 0.6780
Epoch 3/5... Discriminator Loss: 1.4201... Generator Loss: 0.6460
Epoch 3/5... Discriminator Loss: 1.4270... Generator Loss: 0.6381
Epoch 3/5... Discriminator Loss: 1.4224... Generator Loss: 0.6906
Epoch 3/5... Discriminator Loss: 1.4155... Generator Loss: 0.7109
Epoch 3/5... Discriminator Loss: 1.4346... Generator Loss: 0.7999
Epoch 3/5... Discriminator Loss: 1.3970... Generator Loss: 0.6800
Epoch 3/5... Discriminator Loss: 1.4367... Generator Loss: 0.5580
Epoch 3/5... Discriminator Loss: 1.3993... Generator Loss: 0.6843
Epoch 3/5... Discriminator Loss: 1.3997... Generator Loss: 0.6631
Epoch 3/5... Discriminator Loss: 1.4224... Generator Loss: 0.6242
Epoch 3/5... Discriminator Loss: 1.4536... Generator Loss: 0.5659
Epoch 3/5... Discriminator Loss: 1.4162... Generator Loss: 0.6717
Epoch 3/5... Discriminator Loss: 1.4181... Generator Loss: 0.6298
Epoch 3/5... Discriminator Loss: 1.4173... Generator Loss: 0.7232
Epoch 3/5... Discriminator Loss: 1.4079... Generator Loss: 0.6466
Epoch 3/5... Discriminator Loss: 1.4403... Generator Loss: 0.6950
Epoch 3/5... Discriminator Loss: 1.4210... Generator Loss: 0.6725
Epoch 3/5... Discriminator Loss: 1.4296... Generator Loss: 0.6120
Epoch 3/5... Discriminator Loss: 1.4364... Generator Loss: 0.5608
Epoch 3/5... Discriminator Loss: 1.4105... Generator Loss: 0.6579
Epoch 3/5... Discriminator Loss: 1.4115... Generator Loss: 0.6575
Epoch 3/5... Discriminator Loss: 1.4082... Generator Loss: 0.6873
Epoch 3/5... Discriminator Loss: 1.4113... Generator Loss: 0.6106
Epoch 3/5... Discriminator Loss: 1.4011... Generator Loss: 0.6189
Epoch 3/5... Discriminator Loss: 1.4100... Generator Loss: 0.6068
Epoch 3/5... Discriminator Loss: 1.4247... Generator Loss: 0.7088
Epoch 3/5... Discriminator Loss: 1.4048... Generator Loss: 0.6924
Epoch 3/5... Discriminator Loss: 1.4102... Generator Loss: 0.6418
Epoch 3/5... Discriminator Loss: 1.4368... Generator Loss: 0.6496
Epoch 3/5... Discriminator Loss: 1.4136... Generator Loss: 0.7022
Epoch 3/5... Discriminator Loss: 1.4185... Generator Loss: 0.6430
Epoch 3/5... Discriminator Loss: 1.4207... Generator Loss: 0.6485
Epoch 3/5... Discriminator Loss: 1.4214... Generator Loss: 0.6959
Epoch 3/5... Discriminator Loss: 1.4180... Generator Loss: 0.7076
Epoch 3/5... Discriminator Loss: 1.4131... Generator Loss: 0.6242
Epoch 3/5... Discriminator Loss: 1.4100... Generator Loss: 0.6662
Epoch 3/5... Discriminator Loss: 1.4178... Generator Loss: 0.6607
Epoch 3/5... Discriminator Loss: 1.4068... Generator Loss: 0.6430
Epoch 3/5... Discriminator Loss: 1.4182... Generator Loss: 0.6921
Epoch 3/5... Discriminator Loss: 1.4064... Generator Loss: 0.7005
Epoch 3/5... Discriminator Loss: 1.4144... Generator Loss: 0.7582
Epoch 3/5... Discriminator Loss: 1.4234... Generator Loss: 0.5959
Epoch 3/5... Discriminator Loss: 1.4168... Generator Loss: 0.6430
Epoch 3/5... Discriminator Loss: 1.4192... Generator Loss: 0.6483
Epoch 3/5... Discriminator Loss: 1.4141... Generator Loss: 0.6520
Epoch 3/5... Discriminator Loss: 1.4227... Generator Loss: 0.6500
Epoch 3/5... Discriminator Loss: 1.4083... Generator Loss: 0.6947
Epoch 3/5... Discriminator Loss: 1.4345... Generator Loss: 0.5218
Epoch 3/5... Discriminator Loss: 1.3947... Generator Loss: 0.7337
Epoch 3/5... Discriminator Loss: 1.4172... Generator Loss: 0.6377
Epoch 3/5... Discriminator Loss: 1.4126... Generator Loss: 0.6071
Epoch 3/5... Discriminator Loss: 1.4132... Generator Loss: 0.6358
Epoch 3/5... Discriminator Loss: 1.4127... Generator Loss: 0.6313
Epoch 3/5... Discriminator Loss: 1.4103... Generator Loss: 0.6548
Epoch 3/5... Discriminator Loss: 1.3950... Generator Loss: 0.6639
Epoch 3/5... Discriminator Loss: 1.4170... Generator Loss: 0.6124
Epoch 3/5... Discriminator Loss: 1.4275... Generator Loss: 0.6172
Epoch 3/5... Discriminator Loss: 1.4181... Generator Loss: 0.6112
Epoch 3/5... Discriminator Loss: 1.4092... Generator Loss: 0.6114
Epoch 3/5... Discriminator Loss: 1.4190... Generator Loss: 0.6143
Epoch 3/5... Discriminator Loss: 1.4059... Generator Loss: 0.6214
Epoch 3/5... Discriminator Loss: 1.4159... Generator Loss: 0.6259
Epoch 3/5... Discriminator Loss: 1.4121... Generator Loss: 0.6556
Epoch 3/5... Discriminator Loss: 1.4172... Generator Loss: 0.6129
Epoch 3/5... Discriminator Loss: 1.4223... Generator Loss: 0.6208
Epoch 3/5... Discriminator Loss: 1.4121... Generator Loss: 0.6677
Epoch 3/5... Discriminator Loss: 1.4197... Generator Loss: 0.6388
Epoch 3/5... Discriminator Loss: 1.4182... Generator Loss: 0.6381
Epoch 3/5... Discriminator Loss: 1.4166... Generator Loss: 0.6887
Epoch 3/5... Discriminator Loss: 1.4176... Generator Loss: 0.6608
Epoch 3/5... Discriminator Loss: 1.4014... Generator Loss: 0.6966
Epoch 3/5... Discriminator Loss: 1.4161... Generator Loss: 0.6348
Epoch 3/5... Discriminator Loss: 1.4072... Generator Loss: 0.6482
Epoch 3/5... Discriminator Loss: 1.4147... Generator Loss: 0.6444
Epoch 3/5... Discriminator Loss: 1.4180... Generator Loss: 0.6609
Epoch 3/5... Discriminator Loss: 1.4189... Generator Loss: 0.6839
Epoch 3/5... Discriminator Loss: 1.4291... Generator Loss: 0.6594
Epoch 3/5... Discriminator Loss: 1.4291... Generator Loss: 0.6812
Epoch 3/5... Discriminator Loss: 1.4231... Generator Loss: 0.7401
Epoch 3/5... Discriminator Loss: 1.4157... Generator Loss: 0.6654
Epoch 3/5... Discriminator Loss: 1.4045... Generator Loss: 0.6653
Epoch 3/5... Discriminator Loss: 1.4178... Generator Loss: 0.7066
Epoch 3/5... Discriminator Loss: 1.4195... Generator Loss: 0.6237
Epoch 3/5... Discriminator Loss: 1.4218... Generator Loss: 0.6000
Epoch 3/5... Discriminator Loss: 1.4276... Generator Loss: 0.6314
Epoch 3/5... Discriminator Loss: 1.4085... Generator Loss: 0.7043
Epoch 3/5... Discriminator Loss: 1.4105... Generator Loss: 0.6230
Epoch 3/5... Discriminator Loss: 1.4233... Generator Loss: 0.6313
Epoch 3/5... Discriminator Loss: 1.4013... Generator Loss: 0.6768
Epoch 3/5... Discriminator Loss: 1.4444... Generator Loss: 0.6219
Epoch 3/5... Discriminator Loss: 1.4201... Generator Loss: 0.6214
Epoch 3/5... Discriminator Loss: 1.4117... Generator Loss: 0.7289
Epoch 3/5... Discriminator Loss: 1.4026... Generator Loss: 0.6674
Epoch 3/5... Discriminator Loss: 1.4113... Generator Loss: 0.6933
Epoch 3/5... Discriminator Loss: 1.4193... Generator Loss: 0.6597
Epoch 3/5... Discriminator Loss: 1.4075... Generator Loss: 0.7161
Epoch 3/5... Discriminator Loss: 1.4220... Generator Loss: 0.6476
Epoch 3/5... Discriminator Loss: 1.4092... Generator Loss: 0.6368
Epoch 3/5... Discriminator Loss: 1.4117... Generator Loss: 0.6579
Epoch 3/5... Discriminator Loss: 1.4064... Generator Loss: 0.6393
Epoch 3/5... Discriminator Loss: 1.4126... Generator Loss: 0.6052
Epoch 3/5... Discriminator Loss: 1.4098... Generator Loss: 0.6658
Epoch 3/5... Discriminator Loss: 1.3989... Generator Loss: 0.6655
Epoch 3/5... Discriminator Loss: 1.4135... Generator Loss: 0.6777
Epoch 3/5... Discriminator Loss: 1.4021... Generator Loss: 0.6652
Epoch 3/5... Discriminator Loss: 1.4088... Generator Loss: 0.6800
Epoch 3/5... Discriminator Loss: 1.4036... Generator Loss: 0.7360
Epoch 3/5... Discriminator Loss: 1.4314... Generator Loss: 0.7054
Epoch 4/5... Discriminator Loss: 1.4147... Generator Loss: 0.6273
Epoch 4/5... Discriminator Loss: 1.4127... Generator Loss: 0.6114
Epoch 4/5... Discriminator Loss: 1.4177... Generator Loss: 0.6334
Epoch 4/5... Discriminator Loss: 1.4130... Generator Loss: 0.6905
Epoch 4/5... Discriminator Loss: 1.4235... Generator Loss: 0.6990
Epoch 4/5... Discriminator Loss: 1.4175... Generator Loss: 0.5896
Epoch 4/5... Discriminator Loss: 1.4055... Generator Loss: 0.6647
Epoch 4/5... Discriminator Loss: 1.3986... Generator Loss: 0.6485
Epoch 4/5... Discriminator Loss: 1.4198... Generator Loss: 0.6612
Epoch 4/5... Discriminator Loss: 1.4137... Generator Loss: 0.6702
Epoch 4/5... Discriminator Loss: 1.4099... Generator Loss: 0.6714
Epoch 4/5... Discriminator Loss: 1.4121... Generator Loss: 0.6831
Epoch 4/5... Discriminator Loss: 1.3977... Generator Loss: 0.6668
Epoch 4/5... Discriminator Loss: 1.4333... Generator Loss: 0.6304
Epoch 4/5... Discriminator Loss: 1.4011... Generator Loss: 0.6415
Epoch 4/5... Discriminator Loss: 1.4188... Generator Loss: 0.6658
Epoch 4/5... Discriminator Loss: 1.3963... Generator Loss: 0.6870
Epoch 4/5... Discriminator Loss: 1.4308... Generator Loss: 0.6715
Epoch 4/5... Discriminator Loss: 1.4049... Generator Loss: 0.6604
Epoch 4/5... Discriminator Loss: 1.4177... Generator Loss: 0.6445
Epoch 4/5... Discriminator Loss: 1.4064... Generator Loss: 0.6844
Epoch 4/5... Discriminator Loss: 1.4199... Generator Loss: 0.6315
Epoch 4/5... Discriminator Loss: 1.4377... Generator Loss: 0.7188
Epoch 4/5... Discriminator Loss: 1.4080... Generator Loss: 0.6358
Epoch 4/5... Discriminator Loss: 1.4083... Generator Loss: 0.6606
Epoch 4/5... Discriminator Loss: 1.4304... Generator Loss: 0.5888
Epoch 4/5... Discriminator Loss: 1.4169... Generator Loss: 0.7306
Epoch 4/5... Discriminator Loss: 1.4183... Generator Loss: 0.6218
Epoch 4/5... Discriminator Loss: 1.4119... Generator Loss: 0.6379
Epoch 4/5... Discriminator Loss: 1.4086... Generator Loss: 0.6115
Epoch 4/5... Discriminator Loss: 1.4192... Generator Loss: 0.6207
Epoch 4/5... Discriminator Loss: 1.4038... Generator Loss: 0.6875
Epoch 4/5... Discriminator Loss: 1.4224... Generator Loss: 0.6109
Epoch 4/5... Discriminator Loss: 1.4106... Generator Loss: 0.6013
Epoch 4/5... Discriminator Loss: 1.4212... Generator Loss: 0.6376
Epoch 4/5... Discriminator Loss: 1.4187... Generator Loss: 0.6980
Epoch 4/5... Discriminator Loss: 1.4168... Generator Loss: 0.6136
Epoch 4/5... Discriminator Loss: 1.4098... Generator Loss: 0.6359
Epoch 4/5... Discriminator Loss: 1.4267... Generator Loss: 0.6569
Epoch 4/5... Discriminator Loss: 1.4089... Generator Loss: 0.6893
Epoch 4/5... Discriminator Loss: 1.4071... Generator Loss: 0.6667
Epoch 4/5... Discriminator Loss: 1.4153... Generator Loss: 0.5987
Epoch 4/5... Discriminator Loss: 1.4169... Generator Loss: 0.6838
Epoch 4/5... Discriminator Loss: 1.4124... Generator Loss: 0.6890
Epoch 4/5... Discriminator Loss: 1.4251... Generator Loss: 0.6639
Epoch 4/5... Discriminator Loss: 1.4064... Generator Loss: 0.6831
Epoch 4/5... Discriminator Loss: 1.4204... Generator Loss: 0.6881
Epoch 4/5... Discriminator Loss: 1.4081... Generator Loss: 0.7365
Epoch 4/5... Discriminator Loss: 1.3985... Generator Loss: 0.6671
Epoch 4/5... Discriminator Loss: 1.4127... Generator Loss: 0.6170
Epoch 4/5... Discriminator Loss: 1.4185... Generator Loss: 0.6796
Epoch 4/5... Discriminator Loss: 1.3932... Generator Loss: 0.6318
Epoch 4/5... Discriminator Loss: 1.4186... Generator Loss: 0.6233
Epoch 4/5... Discriminator Loss: 1.4322... Generator Loss: 0.6650
Epoch 4/5... Discriminator Loss: 1.4147... Generator Loss: 0.7112
Epoch 4/5... Discriminator Loss: 1.4227... Generator Loss: 0.6017
Epoch 4/5... Discriminator Loss: 1.4227... Generator Loss: 0.7041
Epoch 4/5... Discriminator Loss: 1.4256... Generator Loss: 0.6604
Epoch 4/5... Discriminator Loss: 1.4061... Generator Loss: 0.6543
Epoch 4/5... Discriminator Loss: 1.4216... Generator Loss: 0.6048
Epoch 4/5... Discriminator Loss: 1.4086... Generator Loss: 0.6163
Epoch 4/5... Discriminator Loss: 1.4234... Generator Loss: 0.6045
Epoch 4/5... Discriminator Loss: 1.4040... Generator Loss: 0.6724
Epoch 4/5... Discriminator Loss: 1.4278... Generator Loss: 0.6065
Epoch 4/5... Discriminator Loss: 1.4204... Generator Loss: 0.6486
Epoch 4/5... Discriminator Loss: 1.4048... Generator Loss: 0.6789
Epoch 4/5... Discriminator Loss: 1.4103... Generator Loss: 0.6264
Epoch 4/5... Discriminator Loss: 1.4254... Generator Loss: 0.6590
Epoch 4/5... Discriminator Loss: 1.4098... Generator Loss: 0.7037
Epoch 4/5... Discriminator Loss: 1.4233... Generator Loss: 0.6076
Epoch 4/5... Discriminator Loss: 1.4125... Generator Loss: 0.6464
Epoch 4/5... Discriminator Loss: 1.4096... Generator Loss: 0.6701
Epoch 4/5... Discriminator Loss: 1.4317... Generator Loss: 0.6471
Epoch 4/5... Discriminator Loss: 1.4387... Generator Loss: 0.6287
Epoch 4/5... Discriminator Loss: 1.4209... Generator Loss: 0.6964
Epoch 4/5... Discriminator Loss: 1.4088... Generator Loss: 0.7488
Epoch 4/5... Discriminator Loss: 1.4053... Generator Loss: 0.6178
Epoch 4/5... Discriminator Loss: 1.4135... Generator Loss: 0.7072
Epoch 4/5... Discriminator Loss: 1.4243... Generator Loss: 0.5726
Epoch 4/5... Discriminator Loss: 1.4325... Generator Loss: 0.6437
Epoch 4/5... Discriminator Loss: 1.4128... Generator Loss: 0.6471
Epoch 4/5... Discriminator Loss: 1.4142... Generator Loss: 0.6325
Epoch 4/5... Discriminator Loss: 1.4220... Generator Loss: 0.6043
Epoch 4/5... Discriminator Loss: 1.4131... Generator Loss: 0.6497
Epoch 4/5... Discriminator Loss: 1.4015... Generator Loss: 0.6693
Epoch 4/5... Discriminator Loss: 1.4062... Generator Loss: 0.6592
Epoch 4/5... Discriminator Loss: 1.4307... Generator Loss: 0.5889
Epoch 4/5... Discriminator Loss: 1.4164... Generator Loss: 0.6385
Epoch 4/5... Discriminator Loss: 1.4093... Generator Loss: 0.6460
Epoch 4/5... Discriminator Loss: 1.4110... Generator Loss: 0.6646
Epoch 4/5... Discriminator Loss: 1.4209... Generator Loss: 0.6802
Epoch 4/5... Discriminator Loss: 1.4179... Generator Loss: 0.6306
Epoch 4/5... Discriminator Loss: 1.4362... Generator Loss: 0.5561
Epoch 4/5... Discriminator Loss: 1.4205... Generator Loss: 0.7112
Epoch 4/5... Discriminator Loss: 1.4215... Generator Loss: 0.6404
Epoch 4/5... Discriminator Loss: 1.4193... Generator Loss: 0.6777
Epoch 4/5... Discriminator Loss: 1.4129... Generator Loss: 0.7055
Epoch 4/5... Discriminator Loss: 1.4275... Generator Loss: 0.6063
Epoch 4/5... Discriminator Loss: 1.4250... Generator Loss: 0.6171
Epoch 4/5... Discriminator Loss: 1.4285... Generator Loss: 0.6557
Epoch 4/5... Discriminator Loss: 1.4143... Generator Loss: 0.6382
Epoch 4/5... Discriminator Loss: 1.4121... Generator Loss: 0.6651
Epoch 4/5... Discriminator Loss: 1.4142... Generator Loss: 0.6181
Epoch 4/5... Discriminator Loss: 1.4059... Generator Loss: 0.6381
Epoch 4/5... Discriminator Loss: 1.4227... Generator Loss: 0.6327
Epoch 4/5... Discriminator Loss: 1.4146... Generator Loss: 0.6215
Epoch 4/5... Discriminator Loss: 1.4196... Generator Loss: 0.6116
Epoch 4/5... Discriminator Loss: 1.4195... Generator Loss: 0.6838
Epoch 4/5... Discriminator Loss: 1.4158... Generator Loss: 0.6534
Epoch 4/5... Discriminator Loss: 1.4078... Generator Loss: 0.6498
Epoch 4/5... Discriminator Loss: 1.4069... Generator Loss: 0.6320
Epoch 4/5... Discriminator Loss: 1.4240... Generator Loss: 0.6766
Epoch 4/5... Discriminator Loss: 1.4153... Generator Loss: 0.6877
Epoch 4/5... Discriminator Loss: 1.4045... Generator Loss: 0.6213
Epoch 4/5... Discriminator Loss: 1.4043... Generator Loss: 0.6500
Epoch 4/5... Discriminator Loss: 1.4143... Generator Loss: 0.6678
Epoch 4/5... Discriminator Loss: 1.4223... Generator Loss: 0.6246
Epoch 4/5... Discriminator Loss: 1.4034... Generator Loss: 0.6672
Epoch 4/5... Discriminator Loss: 1.4277... Generator Loss: 0.6565
Epoch 4/5... Discriminator Loss: 1.4115... Generator Loss: 0.6382
Epoch 4/5... Discriminator Loss: 1.4214... Generator Loss: 0.6432
Epoch 4/5... Discriminator Loss: 1.4309... Generator Loss: 0.6132
Epoch 4/5... Discriminator Loss: 1.4151... Generator Loss: 0.6161
Epoch 4/5... Discriminator Loss: 1.4143... Generator Loss: 0.6992
Epoch 4/5... Discriminator Loss: 1.4104... Generator Loss: 0.6857
Epoch 4/5... Discriminator Loss: 1.4296... Generator Loss: 0.6083
Epoch 4/5... Discriminator Loss: 1.4217... Generator Loss: 0.6100
Epoch 5/5... Discriminator Loss: 1.4196... Generator Loss: 0.7072
Epoch 5/5... Discriminator Loss: 1.4303... Generator Loss: 0.5910
Epoch 5/5... Discriminator Loss: 1.4307... Generator Loss: 0.5779
Epoch 5/5... Discriminator Loss: 1.4054... Generator Loss: 0.7177
Epoch 5/5... Discriminator Loss: 1.4110... Generator Loss: 0.6686
Epoch 5/5... Discriminator Loss: 1.4196... Generator Loss: 0.6437
Epoch 5/5... Discriminator Loss: 1.4352... Generator Loss: 0.5856
Epoch 5/5... Discriminator Loss: 1.4201... Generator Loss: 0.6838
Epoch 5/5... Discriminator Loss: 1.4121... Generator Loss: 0.6373
Epoch 5/5... Discriminator Loss: 1.4279... Generator Loss: 0.6344
Epoch 5/5... Discriminator Loss: 1.4078... Generator Loss: 0.6680
Epoch 5/5... Discriminator Loss: 1.4060... Generator Loss: 0.6150
Epoch 5/5... Discriminator Loss: 1.4036... Generator Loss: 0.6704
Epoch 5/5... Discriminator Loss: 1.4191... Generator Loss: 0.7319
Epoch 5/5... Discriminator Loss: 1.4333... Generator Loss: 0.6687
Epoch 5/5... Discriminator Loss: 1.4101... Generator Loss: 0.6884
Epoch 5/5... Discriminator Loss: 1.4256... Generator Loss: 0.6479
Epoch 5/5... Discriminator Loss: 1.4167... Generator Loss: 0.5977
Epoch 5/5... Discriminator Loss: 1.4171... Generator Loss: 0.6235
Epoch 5/5... Discriminator Loss: 1.4136... Generator Loss: 0.6814
Epoch 5/5... Discriminator Loss: 1.4040... Generator Loss: 0.6520
Epoch 5/5... Discriminator Loss: 1.4129... Generator Loss: 0.6641
Epoch 5/5... Discriminator Loss: 1.4058... Generator Loss: 0.6874
Epoch 5/5... Discriminator Loss: 1.4271... Generator Loss: 0.6349
Epoch 5/5... Discriminator Loss: 1.4019... Generator Loss: 0.7084
Epoch 5/5... Discriminator Loss: 1.4076... Generator Loss: 0.6183
Epoch 5/5... Discriminator Loss: 1.4085... Generator Loss: 0.6744
Epoch 5/5... Discriminator Loss: 1.4167... Generator Loss: 0.6179
Epoch 5/5... Discriminator Loss: 1.4069... Generator Loss: 0.6950
Epoch 5/5... Discriminator Loss: 1.4071... Generator Loss: 0.6426
Epoch 5/5... Discriminator Loss: 1.4141... Generator Loss: 0.6340
Epoch 5/5... Discriminator Loss: 1.3963... Generator Loss: 0.6443
Epoch 5/5... Discriminator Loss: 1.4208... Generator Loss: 0.6682
Epoch 5/5... Discriminator Loss: 1.4387... Generator Loss: 0.5913
Epoch 5/5... Discriminator Loss: 1.4032... Generator Loss: 0.6431
Epoch 5/5... Discriminator Loss: 1.4212... Generator Loss: 0.6481
Epoch 5/5... Discriminator Loss: 1.4054... Generator Loss: 0.6725
Epoch 5/5... Discriminator Loss: 1.4157... Generator Loss: 0.6391
Epoch 5/5... Discriminator Loss: 1.4003... Generator Loss: 0.6728
Epoch 5/5... Discriminator Loss: 1.4213... Generator Loss: 0.6461
Epoch 5/5... Discriminator Loss: 1.3964... Generator Loss: 0.6333
Epoch 5/5... Discriminator Loss: 1.4175... Generator Loss: 0.6638
Epoch 5/5... Discriminator Loss: 1.4049... Generator Loss: 0.6450
Epoch 5/5... Discriminator Loss: 1.4085... Generator Loss: 0.6235
Epoch 5/5... Discriminator Loss: 1.4074... Generator Loss: 0.6961
Epoch 5/5... Discriminator Loss: 1.4158... Generator Loss: 0.7011
Epoch 5/5... Discriminator Loss: 1.4147... Generator Loss: 0.6980
Epoch 5/5... Discriminator Loss: 1.4147... Generator Loss: 0.6696
Epoch 5/5... Discriminator Loss: 1.4240... Generator Loss: 0.6686
Epoch 5/5... Discriminator Loss: 1.4251... Generator Loss: 0.6394
Epoch 5/5... Discriminator Loss: 1.4186... Generator Loss: 0.6436
Epoch 5/5... Discriminator Loss: 1.4188... Generator Loss: 0.6339
Epoch 5/5... Discriminator Loss: 1.4167... Generator Loss: 0.5980
Epoch 5/5... Discriminator Loss: 1.4118... Generator Loss: 0.6250
Epoch 5/5... Discriminator Loss: 1.4224... Generator Loss: 0.7092
Epoch 5/5... Discriminator Loss: 1.4296... Generator Loss: 0.6618
Epoch 5/5... Discriminator Loss: 1.4206... Generator Loss: 0.6643
Epoch 5/5... Discriminator Loss: 1.4008... Generator Loss: 0.6867
Epoch 5/5... Discriminator Loss: 1.4006... Generator Loss: 0.6675
Epoch 5/5... Discriminator Loss: 1.4088... Generator Loss: 0.6693
Epoch 5/5... Discriminator Loss: 1.4084... Generator Loss: 0.6411
Epoch 5/5... Discriminator Loss: 1.4201... Generator Loss: 0.6680
Epoch 5/5... Discriminator Loss: 1.4131... Generator Loss: 0.6459
Epoch 5/5... Discriminator Loss: 1.4237... Generator Loss: 0.6347
Epoch 5/5... Discriminator Loss: 1.4167... Generator Loss: 0.6221
Epoch 5/5... Discriminator Loss: 1.4151... Generator Loss: 0.6713
Epoch 5/5... Discriminator Loss: 1.4229... Generator Loss: 0.6924
Epoch 5/5... Discriminator Loss: 1.4162... Generator Loss: 0.6956
Epoch 5/5... Discriminator Loss: 1.4212... Generator Loss: 0.7028
Epoch 5/5... Discriminator Loss: 1.4105... Generator Loss: 0.6878
Epoch 5/5... Discriminator Loss: 1.4043... Generator Loss: 0.6687
Epoch 5/5... Discriminator Loss: 1.4067... Generator Loss: 0.6541
Epoch 5/5... Discriminator Loss: 1.4141... Generator Loss: 0.6603
Epoch 5/5... Discriminator Loss: 1.4178... Generator Loss: 0.6433
Epoch 5/5... Discriminator Loss: 1.4116... Generator Loss: 0.7064
Epoch 5/5... Discriminator Loss: 1.4105... Generator Loss: 0.6533
Epoch 5/5... Discriminator Loss: 1.4059... Generator Loss: 0.7067
Epoch 5/5... Discriminator Loss: 1.4084... Generator Loss: 0.6164
Epoch 5/5... Discriminator Loss: 1.4093... Generator Loss: 0.5875
Epoch 5/5... Discriminator Loss: 1.4063... Generator Loss: 0.6485
Epoch 5/5... Discriminator Loss: 1.4251... Generator Loss: 0.5940
Epoch 5/5... Discriminator Loss: 1.4280... Generator Loss: 0.6475
Epoch 5/5... Discriminator Loss: 1.4177... Generator Loss: 0.6654
Epoch 5/5... Discriminator Loss: 1.4138... Generator Loss: 0.6762
Epoch 5/5... Discriminator Loss: 1.4219... Generator Loss: 0.6273
Epoch 5/5... Discriminator Loss: 1.4057... Generator Loss: 0.6406
Epoch 5/5... Discriminator Loss: 1.4065... Generator Loss: 0.6960
Epoch 5/5... Discriminator Loss: 1.4321... Generator Loss: 0.6556
Epoch 5/5... Discriminator Loss: 1.4198... Generator Loss: 0.6542
Epoch 5/5... Discriminator Loss: 1.4182... Generator Loss: 0.7142
Epoch 5/5... Discriminator Loss: 1.4174... Generator Loss: 0.6410
Epoch 5/5... Discriminator Loss: 1.4219... Generator Loss: 0.6949
Epoch 5/5... Discriminator Loss: 1.4238... Generator Loss: 0.6352
Epoch 5/5... Discriminator Loss: 1.4118... Generator Loss: 0.6376
Epoch 5/5... Discriminator Loss: 1.4262... Generator Loss: 0.6036
Epoch 5/5... Discriminator Loss: 1.4098... Generator Loss: 0.6878
Epoch 5/5... Discriminator Loss: 1.4047... Generator Loss: 0.6608
Epoch 5/5... Discriminator Loss: 1.4143... Generator Loss: 0.6437
Epoch 5/5... Discriminator Loss: 1.4246... Generator Loss: 0.6361
Epoch 5/5... Discriminator Loss: 1.4122... Generator Loss: 0.6172
Epoch 5/5... Discriminator Loss: 1.3953... Generator Loss: 0.6750
Epoch 5/5... Discriminator Loss: 1.4082... Generator Loss: 0.6494
Epoch 5/5... Discriminator Loss: 1.4084... Generator Loss: 0.6665
Epoch 5/5... Discriminator Loss: 1.4209... Generator Loss: 0.6626
Epoch 5/5... Discriminator Loss: 1.4236... Generator Loss: 0.6888
Epoch 5/5... Discriminator Loss: 1.4045... Generator Loss: 0.6625
Epoch 5/5... Discriminator Loss: 1.4121... Generator Loss: 0.6841
Epoch 5/5... Discriminator Loss: 1.4098... Generator Loss: 0.6495
Epoch 5/5... Discriminator Loss: 1.4239... Generator Loss: 0.6003
Epoch 5/5... Discriminator Loss: 1.4129... Generator Loss: 0.6323
Epoch 5/5... Discriminator Loss: 1.4097... Generator Loss: 0.6779
Epoch 5/5... Discriminator Loss: 1.4095... Generator Loss: 0.7099
Epoch 5/5... Discriminator Loss: 1.4359... Generator Loss: 0.6288
Epoch 5/5... Discriminator Loss: 1.4100... Generator Loss: 0.6574
Epoch 5/5... Discriminator Loss: 1.4130... Generator Loss: 0.7043
Epoch 5/5... Discriminator Loss: 1.3984... Generator Loss: 0.7098
Epoch 5/5... Discriminator Loss: 1.4113... Generator Loss: 0.6556
Epoch 5/5... Discriminator Loss: 1.3974... Generator Loss: 0.7021
Epoch 5/5... Discriminator Loss: 1.4129... Generator Loss: 0.6676
Epoch 5/5... Discriminator Loss: 1.4152... Generator Loss: 0.6205
Epoch 5/5... Discriminator Loss: 1.4206... Generator Loss: 0.6567
Epoch 5/5... Discriminator Loss: 1.4143... Generator Loss: 0.6662
Epoch 5/5... Discriminator Loss: 1.4053... Generator Loss: 0.6606
Epoch 5/5... Discriminator Loss: 1.4194... Generator Loss: 0.6503
Epoch 5/5... Discriminator Loss: 1.4278... Generator Loss: 0.6109
Epoch 5/5... Discriminator Loss: 1.4047... Generator Loss: 0.6681
Epoch 5/5... Discriminator Loss: 1.4171... Generator Loss: 0.7089

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.